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Category Archives: Neural Networks

Around a halve year back I stumbled over Kaggle.com, a vital community portal of Artificial Intelligence and machine learning experts. Kaggle not only encourages people around the world to share thoughts and example data sets on popular machine learning tasks, they also host great AI challenges.

Since I joined the Kaggle community 6 month ago, I was fascinated about the individual challenges that were published. Those challenges range from predicting Mercari product prices over detecting icebergs from radar data to speech recognition tasks.

Many companies such as Google, Mercari or Zillow are hosting challenges where more than thousand of teams try to predict the best results. Often it is unbelievable how those teams solve these complex machine learning tasks.

Besides providing the challenges and the data sets necessary to wake the interest of global leaders within the machine learning and AI community, Kaggle also offers a tremendously powerful kernel execution environment. This execution environment consists of preconfigured Docker containers that were specifically designed for training models. In order to design and execute a machine learning kernel you simply edit the code online (Python, R, Notebook) and execute it within the Kaggle infrastructure.

As Kaggle docker containers are completely preconfigured you save a lot of time to download and prepare your environment.

Kaggle really pushes the AI community forward in terms of offering a flexible and open platform for executing kernels and to quickly get hands on interesting data sets. The community platform also does a pretty good job in bringing the global community together and stimulates a broader and practical discussion outside the theoretical scientific community.

Besides if you need a quick start tutorial on how to train your first neural network, grab my eBook at Amazon:

SLAC and Stanford university recently announced a breakthrough of using neural networks, one of the base algorithms of Artificial Intelligence, to spped up their data analysis effort. The spacetime data SLAC and Standford analysis is crucial for the understanding of the universe. By using neural networks to analyze those complex distortions in spacetime known as gravitational lenses the Stanford researchers were able to analyze the data 10 million times faster than traditional methods.
The researchers fed a neural network with half a million of images of gravitational lenses, which typically takes a day. Once the training process is finished, the trained AI neural network is capable of detecting similar lenses within a fraction of a second. The precision of the newly introduced Artificial Intelligence based methodology is comparable to the traditional approach that took weeks to finish.
This is another application domain where Artificial Intelligence helps to speed up traditional analysis methods from taking month to less than a second. We can expect that the analysis of spacetime anomalies will gain a lot of traction, now that the analysis process does not take years. Refer to the original press release here.
If you are interested into how artificial neural networks are implemented, read my Kindle eBook on ‘Applied Artificial Intelligence’.